Electronic navigation has become popular for automotive and recreational uses, as GPS devices evolve to the consumer level. Indoor environments, however, such as for robotic uses, employ different approaches. Navigation in an unknown indoor environment requires reliable localization. Unlike outdoor environments, there is limited assistance from external agents such as GPS which provide information about the absolute position of the system. Simultaneous Localization and Mapping (SLAM) was developed to address the issue of localization in an environment where pre-cached maps and absolute pose information are not available.
Vision based SLAM (VSLAM) has gained popularity due to the availability of inexpensive cameras and low cost high speed processors. Most VSLAM algorithms rely on repeated observation of distinct features to determine the camera pose. The stereo disparity observed using nearby features provides information about translation of the camera while the disparity due to distant features provides information about the orientation, or pose, of the camera.